The bovine annexin 9 gene (ANXA9) is significantly associated with milk-fat yield in a Spanish Holstein-Friesian population

The impact of genetic variants related to the fatty acid metabolic process pathway on milk production traits in Jersey cows

The synthesis of fatty acids plays a critical role in shaping milk production characteristics in dairy cattle. Thus, identifying effective haplotypes within the fatty acid metabolism pathway will provide novel and robust insights into the genetics of dairy cattle. This study aimed to comprehensively examine the individual and combined impacts of fundamental genes within the fatty acid metabolic process pathway in Jersey cows. A comprehensive phenotypic dataset was compiled, considering milk production traits, to summarize a cow's productivity across three lactations. Genotyping was conducted through PCR-RFLP and Sanger sequencing, while the association between genotype and phenotype was quantified using linear mixed models. Moderate biodiversity and abundant variation suitable for haplotype analysis were observed across all examined markers. The individual effects of the FABP3, LTF and ANXA9 genes significantly influenced both milk yield and milk fat production. Additionally, this study reveals novel two-way interactions between genes in the fatty acid metabolism pathway that directly affect milk fat properties. Notably, we identified that the GGAAGG haplotype in FABP3×LTF×ANXA9 interaction may be a robust genetic marker concerning both milk fat yield and percentage. Consequently, the genotype combinations highlighted in this study serve as novel and efficient markers for assessing the fat content in cow's milk.

FASN, SCD1 and ANXA9 gene polymorphism as genetic predictors of the fatty acid profile of sheep milk

In this study, single nucleotide polymorphisms (SNPs) in the ANXA9 (annexin 9), FASN (fatty acid synthase) and SCD1 (stearoyl-CoA desaturase 1) genes were analyzed as factors influencing fatty acid profiles in milk from Zošľachtená valaška sheep. SNP in selected genes was identified using polymerase chain reaction (PCR) and restriction fragment length polymorphism (PCR–RFLP). The long-chain fatty acids profile in sheep milk was identified by gas chromatography. Statistical analysis of the SCD1/Cfr13I polymorphism showed that the milk of the homozygous AA animals was characterized by a lower (P < 0.05) share of C4:0, C6:0, C8:0, C10:0, C12:0, C14:0 in comparison to the homozygous CC sheep. The milk of heterozygous sheep was characterized by a higher (P < 0.05) proportion of C13:0 acid compared to the milk of sheep with the homozygous AA type. A higher (P < 0.05) level of saturated fatty acids (SFA) was found in the milk of CC genotype sheep compared to the AA genotype. Our results lead to the conclusion that the greatest changes were observed for the SCD1/Cfr13I polymorphism and the least significant ones for FASN/AciI. Moreover, it is the first evidence that milk from sheep with SCD1/Cfr13I polymorphism and the homozygous AA genotype showed the most desirable fatty acids profile.

Potential candidate gene markers for milk fat in bovines: A review

In dairy animals, the principal goal of selection is to improve quality and quantity of milk. Genetic information inferred from single nucleotide polymorphism (SNP) primarily linked to Quantitative Trait Loci (QTL) can be used to improve selection for milk and milk constituent traits in bovines. Selection for a marker allele known to be associated with a beneficial QTL increases the frequency of that allele and hence, dairy performance can be enhanced. One of the potential benefit of selection based on molecular marker is that the marker genotypes can be determined in a dairy animal just after birth. Thus, marker information can be used to predict an animal's genotype before its actual performance recording for a trait is available, which considerably reduces generation interval and thus improves genetic gain in a herd for milk and its constituent traits. This review article is an attempt to comprehend the idea behind marker based selection for milk fat and genes regulating milk fat with significant effects that can be targeted specifically in selection of superior dairy animals. Once an association is established, itcan be utilized in a marker assisted breeding program for improvement of bovines.

Polymorphism in ovine ANXA9 gene and the physio-chemical properties and the fraction of protein in milk

BACKGROUND

Annexin A9 (ANXA9) is a specific fatty acid transport protein. The ANXA9 gene is expressed in various tissues, including secretory tissue and the mammary glands. The association between the three single nucleotide polymorphisms (SNPs) of the ANXA9 gene and sheep's milk composition was assessed.

RESULTS

Genotype analysis was performed using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. The ANXA9 polymorphisms that were studied had the following major allele frequencies (MAFs): SNP1: allele G 0,66; SNP2: allele G 0,54; SNP3: allele C 0,57. The study found the most favorable profile of protein fractions, namely increased kappa-casein fractions and a decreased level of whey protein in sheep's milk for the SNP1 and SNP3 polymorphisms. Sheep with the SNP1 GA genotype had the highest (P < 0.05) content of fat and dry matter in milk. AXNA9 gene polymorphism did not influence the levels of protein, lactose, or urea in sheep's milk.

CONCLUSION

The information contained in this study may be useful for determining the impact of the ANXA9 gene on sheep's milk. The ANXA9 SNP1 and SNP3 polymorphism results could be included in breeding programs to select sheep with the genotypes ensuring the highest kappa-casein levels in milk. However, it is worth conducting further research on ANXA9 and milk composition in larger herds of animals and various breeds of sheep. © 2018 Society of Chemical Industry.

Detection of quantitative trait loci for maternal traits using high-density genotypes of Blonde d'Aquitaine beef cattle

Background

The genetic determinism of the calving and suckling performance of beef cows is little known whereas these maternal traits are of major economic importance in beef cattle production systems. This paper aims to identify QTL regions and candidate genes that affect maternal performance traits in the Blonde d’Aquitaine breed. Three calving performance traits were studied: the maternal effect on calving score from field data, the calving score and pelvic opening recorded in station for primiparous cows. Three other traits related to suckling performance were also analysed: the maternal effect on weaning weight from field data, milk yield and the udder swelling score recorded in station for primiparous cows. A total of 2,505 animals were genotyped from various chip densities and imputed in high density chips for 706,791 SNP. The number of genotyped animals with phenotypes ranged from 1,151 to 2,284, depending on the trait considered.

Results

QTL detections were performed using a Bayes C approach. Evidence for a QTL was based on Bayes Factor values. Putative candidate genes were proposed for the QTL with major evidence for one of the six traits and for the QTL shared by at least two of the three traits underlying either calving or suckling performance. Nine candidate genes were proposed for calving performance among the nine highlighted QTL regions. The neuroregulin gene on chromosome 27 was notably identified as a very likely candidate gene for maternal calving performance. As for suckling abilities, seven candidate genes were identified among the 15 highlighted QTL. In particular, the Group-Specific Component gene on chromosome 6, which encodes vitamin D binding protein, is likely to have a major effect on maternal weaning weight in the Blonde d’Aquitaine breed. This gene had already been linked to milk production and clinical mastitis in dairy cattle.

Conclusion

In the near future, these QTL findings and the preliminary proposals of candidate genes which act on the maternal performance of beef cows should help to identify putative causal mutations based on sequence data from different cattle breeds.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0397-y) contains supplementary material, which is available to authorized users.

A unified framework for association analysis with multiple related phenotypes

We consider the problem of assessing associations between multiple related outcome variables, and a single explanatory variable of interest. This problem arises in many settings, including genetic association studies, where the explanatory variable is genotype at a genetic variant. We outline a framework for conducting this type of analysis, based on Bayesian model comparison and model averaging for multivariate regressions. This framework unifies several common approaches to this problem, and includes both standard univariate and standard multivariate association tests as special cases. The framework also unifies the problems of testing for associations and explaining associations – that is, identifying which outcome variables are associated with genotype. This provides an alternative to the usual, but conceptually unsatisfying, approach of resorting to univariate tests when explaining and interpreting significant multivariate findings. The method is computationally tractable genome-wide for modest numbers of phenotypes (e.g. 5–10), and can be applied to summary data, without access to raw genotype and phenotype data. We illustrate the methods on both simulated examples, and to a genome-wide association study of blood lipid traits where we identify 18 potential novel genetic associations that were not identified by univariate analyses of the same data.